Embracing the 3-Month Rule: A Practical Approach to Innovative Development
In the realm of software development, one often hears the mantra of Paul Graham: “Do things that don’t scale.” Yet, the conversation rarely dives into how to effectively apply this philosophy, especially in the world of coding. After eight months of developing my AI podcast platform, I’ve crafted a straightforward framework that I like to call the “3-Month Rule.” This approach dictates that any unscalable solution has a lifespan of three months. At the end of this period, it will either solidify its worth and be refined, or it will be abandoned.
As developers, we frequently aim for scalable solutions right from the beginning—think design patterns, microservices, and robust architectures designed to handle significant user volumes. However, this mindset is often more aligned with larger companies than with startups, where spending time on scalable code can sometimes equate to procrastination. We often end up optimizing for users that we don’t yet have and addressing challenges that may never arise. My 3-Month Rule compels me to focus on straightforward, even “imperfect” code that gets products out the door, allowing me to discover the genuine needs of my users.
My Non-Traditional Infrastructure Strategies and Their Benefits
1. Consolidating into a Single Virtual Machine
My entire application ecosystem—database, web server, background tasks—operates from a single virtual machine, costing me just $40 a month. There’s no redundancy and backups are manually transferred to my local machine.
You might wonder why this is a wise approach: In just two months, I’ve gained more insights into my resource requirements than any capacity planning document could yield. I found that my “AI-intensive” platform only peaks at 4GB of RAM, revealing that the complex Kubernetes setup I nearly implemented would have been managing largely idle containers. When crashes (which have happened a couple of times) occur, they provide real insights into unexpected breakdowns.
2. Using Hardcoded Configuration
My configuration looks something like this:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
No separate configuration files, no environment variables—just constants dispersed throughout my code. Making changes requires a redeploy.
The genius in this approach? I can quickly search my codebase for configuration values
